--- title: "hello-agents vs Kiln" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-kiln-ai-kiln" tools: ["datawhalechina-hello-agents", "kiln-ai-kiln"] --- # hello-agents vs Kiln *GraphCanon updated Jul 12, 2026* ## Verdict Pick hello-agents if hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods; pick Kiln if kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [Kiln](https://kiln.tech) has 5.0k stars, 375 forks, and 63 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [Kiln's repository](https://github.com/Kiln-AI/Kiln). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [Kiln](/tools/kiln-ai-kiln.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Build, Evaluate, and Optimize AI Systems | | Stars | 65,432 | 4,960 | | Forks | 8,109 | 375 | | Open issues | 144 | 63 | | Language | Python | Python | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Other | | Categories | AI Agents, LLM Frameworks | AI Agents, Data & Retrieval, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [Kiln](/tools/kiln-ai-kiln.md) | | --- | --- | --- | | Open issues (now) | 144 | 63 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/kiln-ai-kiln/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Decision facts: Kiln - **Adopt for:** Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. ## Choose when ### Choose hello-agents if… - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - Also covers LLM Frameworks. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose Kiln if… - Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation. - Also covers Data & Retrieval, Evaluation & Observability, Model Training. - When you need extensive tools for evaluating custom AI agents ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## When NOT to use Kiln - If your project strictly requires a lightweight tool without comprehensive dataset management options - Avoid if you do not require advanced synthetic data generation capabilities ## Common questions ### What is the difference between hello-agents and Kiln? hello-agents: Course on building intelligent agents from scratch. Kiln: Build, Evaluate, and Optimize AI Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over Kiln? Choose hello-agents over Kiln when Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; Also covers LLM Frameworks; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I choose Kiln over hello-agents? Choose Kiln over hello-agents when Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation; Also covers Data & Retrieval, Evaluation & Observability, Model Training; When you need extensive tools for evaluating custom AI agents. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### When should I avoid Kiln? If your project strictly requires a lightweight tool without comprehensive dataset management options Avoid if you do not require advanced synthetic data generation capabilities ### Is hello-agents or Kiln more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 4,960). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and Kiln open source? Yes - both are open-source projects on GitHub (hello-agents: Other, Kiln: Other). ### Where can I find alternatives to hello-agents or Kiln? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [Kiln alternatives](/tools/kiln-ai-kiln/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [Kiln markdown twin](/tools/kiln-ai-kiln/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/datawhalechina-hello-agents-vs-kiln-ai-kiln.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or Kiln? hello-agents: Very active. Kiln: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for hello-agents and Kiln? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [Kiln trust report](/tools/kiln-ai-kiln/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_